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# Senior Software Engineer, Agents

- **Company:** Harvey, Inc.
- **Location:** New York, NY, United States
- **Experience:** Expert
- **Salary:** $193,400.0 - $290,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Python (Programming Language), Software Engineering, Large Language Models, Multi-Agent Systems, Prompt Engineering, Model Validation, Caching, Backend
- **Published:** July 24, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=4a8aac2d5fbdccff

## About the Role

* Passion for building effective domain-specific agents.
 * Iterative mindset: you develop proof of concepts, make decisions quickly, and ship v0s.
 * Comfortable with when and how to use evaluations to drive quality.
 * Humble and adaptable about code and frameworks. We expect you to drive adoption of new best practices as they develop.
 * 5-8 years (post-BS/MS) of software engineering experience.
 * We are hiring for this role across Mid, Senior, and Staff levels.
 * Proficiency in Python and experience working with LLM APIs and agent frameworks.
 * Experience with shipping user-facing products, either on the backend or full-stack.

## Description

As a Software Engineer, Agents, you'll build the systems that make our AI agents indispensable to legal professionals.

You will design environments and actions for agentic professional work, make model selection decisions, manage context windows, create optimal tools, and develop evals that enable faster iteration loops to unlock new capabilities.

We're looking for engineers and researchers who are immersed in the space, driven to ship impactful products, and are experienced in using practical evaluations to drive task completion quality and customer delight.

What You'll Do

 * Partner with customers and PMs to understand legal workflows, design practical evaluations that capture what "excellent" means, and ship agents that get the job done.
 * Optimize agent performance through prompt engineering, model selection, tool design, skill writing, context window management, and eval harness development.
 * Work with our model infra team to design and implement infrastructure for low-latency agent execution, including caching strategies, parallel tool calls, or subagent patterns.
 * Improve our observability and instrumentation to profile agent behavior, identify bottlenecks, and drive optimization decisions.
 * Stay current on new developments in agentic systems and bring those learnings back to the products we build.


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